84 read parquet

Reading GeoParquet files and visualizing vector data interactively

Uncomment the following line to install leafmap if needed.

In [1]:

# %pip install -U leafmap lonboard==0.3.0

# %pip install -U leafmap lonboard==0.3.0

In [2]:

import leafmap

import leafmap

Visualizing point data.

In [3]:

url = 'https://open.gishub.org/data/duckdb/cities.parquet'

url = 'https://open.gishub.org/data/duckdb/cities.parquet'

Read GeoParquet and return a GeoPandas GeoDataFrame.

In [4]:

gdf = leafmap.read_parquet(url, return_type='gdf', src_crs='EPSG:4326')
gdf.head()

gdf = leafmap.read_parquet(url, return_type='gdf', src_crs='EPSG:4326') gdf.head()

Out[4]:

	country	id	latitude	longitude	name	population	geometry
0	UGA	1.0	0.5833	32.5333	Bombo	75000.0	POINT (32.53330 0.58330)
1	UGA	2.0	0.6710	30.2750	Fort Portal	42670.0	POINT (30.27500 0.67100)
2	ITA	3.0	40.6420	15.7990	Potenza	69060.0	POINT (15.79900 40.64200)
3	ITA	4.0	41.5630	14.6560	Campobasso	50762.0	POINT (14.65600 41.56300)
4	ITA	5.0	45.7370	7.3150	Aosta	34062.0	POINT (7.31500 45.73700)

View the GeoDataFrame interactively using folium.

In [5]:

gdf.explore()

gdf.explore()

Out[5]:

Make this Notebook Trusted to load map: File -> Trust Notebook

Visualize the GeoDataFrame using lonboard.

In [6]:

leafmap.view_vector(gdf, get_radius=20000, get_fill_color='blue')

leafmap.view_vector(gdf, get_radius=20000, get_fill_color='blue')

Visualizing polygon data.

In [7]:

url = 'https://data.source.coop/giswqs/nwi/wetlands/DC_Wetlands.parquet'

url = 'https://data.source.coop/giswqs/nwi/wetlands/DC_Wetlands.parquet'

In [8]:

gdf = leafmap.read_parquet(url, return_type='gdf', src_crs='EPSG:5070', dst_crs='EPSG:4326')
gdf.head()

gdf = leafmap.read_parquet(url, return_type='gdf', src_crs='EPSG:5070', dst_crs='EPSG:4326') gdf.head()

Out[8]:

	ATTRIBUTE	WETLAND_TYPE	ACRES	Shape_Length	Shape_Area	geometry
0	PEM5Ax	Freshwater Emergent Wetland	0.454008	621.865659	1837.304895	MULTIPOLYGON (((-76.88117 38.98540, -76.88112 ...
1	PFO1A	Freshwater Forested/Shrub Wetland	3.339719	586.449553	13515.364977	MULTIPOLYGON (((-76.88955 38.98061, -76.88950 ...
2	PUBHh	Freshwater Pond	0.873177	335.425439	3533.620783	MULTIPOLYGON (((-76.87580 38.98168, -76.87571 ...
3	R4SBC	Riverine	0.788316	1078.745304	3190.202659	MULTIPOLYGON (((-76.87603 38.98196, -76.87607 ...
4	R4SBC	Riverine	0.069572	106.791860	281.546217	MULTIPOLYGON (((-76.87472 38.98170, -76.87484 ...

In [9]:

gdf.explore()

gdf.explore()

Out[9]:

Make this Notebook Trusted to load map: File -> Trust Notebook

In [10]:

leafmap.view_vector(gdf, get_fill_color=[0, 0, 255, 128])

leafmap.view_vector(gdf, get_fill_color=[0, 0, 255, 128])

Alternatively, you can specify a color map to visualize the data.

In [11]:

color_map =  {
        "Freshwater Forested/Shrub Wetland": (0, 136, 55),
        "Freshwater Emergent Wetland": (127, 195, 28),
        "Freshwater Pond": (104, 140, 192),
        "Estuarine and Marine Wetland": (102, 194, 165),
        "Riverine": (1, 144, 191),
        "Lake": (19, 0, 124),
        "Estuarine and Marine Deepwater": (0, 124, 136),
        "Other": (178, 134, 86),
    }

color_map = { "Freshwater Forested/Shrub Wetland": (0, 136, 55), "Freshwater Emergent Wetland": (127, 195, 28), "Freshwater Pond": (104, 140, 192), "Estuarine and Marine Wetland": (102, 194, 165), "Riverine": (1, 144, 191), "Lake": (19, 0, 124), "Estuarine and Marine Deepwater": (0, 124, 136), "Other": (178, 134, 86), }

In [12]:

leafmap.view_vector(gdf, color_column='WETLAND_TYPE', color_map=color_map, opacity=0.5)

leafmap.view_vector(gdf, color_column='WETLAND_TYPE', color_map=color_map, opacity=0.5)

Display a legend for the data.

In [13]:

leafmap.Legend(title="Wetland Type", legend_dict=color_map)

leafmap.Legend(title="Wetland Type", legend_dict=color_map)

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Last update: 2023-11-27
Created: 2023-11-27